我有一个格式如下的文件:
S1A23
0.01,0.01
0.02,0.02
0.03,0.03
S25A123
0.05,0.06
0.07,0.08
S3034A1
1000,0.04
2000,0.08
3000,0.1
我想将其按每个“S_A_”进行分解,并计算下面数据的相关系数。到目前为止,我已经:
import re
import pandas as pd
test = pd.read_csv("predict.csv",sep=('S\d+A\d+'))
print test
但这只能给我:
Unnamed: 0 ,
0 0.01,0.01 None
1 0.02,0.02 None
2 0.03,0.03 None
3 NaN ,
4 0.05,0.06 None
5 0.07,0.08 None
6 NaN ,
7 1000,0.04 None
8 2000,0.08 None
9 3000,0.1 None
[10 rows x 2 columns]
理想情况下,我希望保留正则表达式分隔符,并且有类似的内容:
S1A23: 1.0
S2A123: 0.86
S303A1: 0.75
这可能吗?
编辑
运行大文件(~250k 行)时,我收到以下错误。这不是数据的问题,因为当我将 ~250k 行分成更小的 block 时,所有部分都运行良好。
Traceback (most recent call last):
File "/Users/adamg/PycharmProjects/Subj_AnswerCorrCoef/GetCorrCoef.py", line 15, in <module>
print(result)
File "/Users/adamg/anaconda/lib/python2.7/site-packages/pandas/core/base.py", line 35, in __str__
return self.__bytes__()
File "/Users/adamg/anaconda/lib/python2.7/site-packages/pandas/core/base.py", line 47, in __bytes__
return self.__unicode__().encode(encoding, 'replace')
File "/Users/adamg/anaconda/lib/python2.7/site-packages/pandas/core/series.py", line 857, in __unicode__
result = self._tidy_repr(min(30, max_rows - 4))
TypeError: unsupported operand type(s) for -: 'NoneType' and 'int'
我的确切代码是:
import numpy as np
import pandas as pd
import csv
pd.options.display.max_rows = None
fileName = 'keyStrokeFourgram/TESTING1'
df = pd.read_csv(fileName, names=['pause', 'probability'])
mask = df['pause'].str.match('^S\d+_A\d+')
df['S/A'] = (df['pause']
.where(mask, np.nan)
.fillna(method='ffill'))
df = df.loc[~mask]
result = df.groupby(['S/A']).apply(lambda grp: grp['pause'].corr(grp['probability']))
print(result)
最佳答案
sep
参数用于指定分隔同一行上的值的模式。它不能用于将 csv 的行分隔成单独的数据帧。
编辑:有一种方法可以使用 read_csv
将 csv 读入 DataFrame。这比使用 Python 循环(如我原来的答案中所做的那样)更好,因为 read_csv
should be faster 。这可能很重要——特别是对于大型 csv 文件。
import numpy as np
import pandas as pd
df = pd.read_csv("data", names=['x', 'y'])
mask = df['x'].str.match('^S\d+A\d+') # 1
df['type'] = (df['x']
.where(mask, np.nan) # 2
.fillna(method='ffill')) # 3
df = df.loc[~mask] # 4
result = df.groupby(['type']).apply(lambda grp: grp['x'].corr(grp['y']))
print(result)
产量
type
S1A23 1.000000
S25A123 1.000000
S3034A1 0.981981
dtype: float64
'x'
列中具有“type”的行的掩码为 True。In [139]: mask Out[139]: 0 True 1 False 2 False 3 False 4 True 5 False 6 False 7 True 8 False 9 False 10 False Name: x, dtype: bool
df['x'].where(mask, np.nan)
返回一个 Series,等于df['x']
其中 掩码为 True,否则为 np.nan。用货币值向前填写 nan
In [141]: df['x'].where(mask, np.nan).fillna(method='ffill') Out[141]: 0 S1A23 1 S1A23 2 S1A23 3 S1A23 4 S25A123 5 S25A123 6 S25A123 7 S3034A1 8 S3034A1 9 S3034A1 10 S3034A1 Name: x, dtype: object
- 仅选择掩码为 False 的行
原答案:
不幸的是,我没有找到将数据文件直接读入适当的 DataFrame 的方法。您需要使用 Python 循环对行进行一些调整,使其变成正确的形式。
import pandas as pd
import csv
def to_columns(f):
val = None
for row in csv.reader(f):
if len(row) == 1:
val = row[0]
else:
yield [val] + row
with open('data') as f:
df = pd.DataFrame.from_records(to_columns(f), columns=['type', 'x', 'y'])
print(df)
result = df.groupby(['type']).apply(lambda grp: grp['x'].corr(grp['y']))
print(result)
关于python - 在 Python 中通过正则表达式分解 CSV,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/21755302/